MOTIVATION: Do machine learning methods improve standard deconvolution techniques for gene expression data? This article uses a unique new dataset combined with an open innovation competition to evaluate a wide range of approaches developed by 294 competitors from 20 countries. The competition's objective was to address a deconvolution problem critical to analyzing genetic perturbations from the Connectivity Map. The issue consists of separating gene expression of individual genes from raw measurements obtained from gene pairs. We evaluated the outcomes using ground-truth data (direct measurements for single genes) obtained from the same samples. RESULTS: We find that the top-ranked algorithm, based on random forest regression, beat the other methods in accuracy and reproducibility; more traditional gaussian-mixture methods performed well and tended to be faster, and the best deep learning approach yielded outcomes slightly inferior to the above methods. We anticipate researchers in the field will find the dataset and algorithms developed in this study to be a powerful research tool for benchmarking their deconvolution methods and a resource useful for multiple applications. AVAILABILITY AND IMPLEMENTATION: The data is freely available at clue.io/data (section Contests) and the software is on GitHub at https://github.com/cmap/gene_deconvolution_challenge. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The goal of the 5th Workshop on Open Source Software Engineering is to bring together researchers and practitioners for the purpose of building a roadmap of the ways in which various computing application spaces have been impacted by open source software and also by open source development methods, tools and organizational structures.

Building on the success of "Making Sense of the Bazaar" and "Meeting Challenges and Surviving Success" - the 1/sup st/ and 2/sup nd/ Workshops on Open Source Software Engineering (ICSE 2001 and ICSE 2002)-this workshop ("Taking Stock of the Bazaar') brings together researchers and practitioners for the purpose of discussing the diverse array of techniques - as well as supporting tools and social/organizational contexts which can be observed in the domain of open source software.